Collaborative Design for Immersive Visual Analytics
Ulrich Engelke, Senior Research Scientist
Peter Marendy, Senior Research Projects Officer
Paulo A. de Souza Junior, OCE Science Leader (affiliate member)
Ray Williams, Visiting Scientist (affiliate member)
2015 – ongoing
Global Initiative for Honey bee Health (GIHH) is an international collaboration effort that aims to make a global impact on the ecosystem and sustainable development. Honey bee behaviours are studied based on the data collected from microsensor backpacks carried by the bees. A big database is being gathered on a global scale and will be analysed to provide knowledge and insight into honey bee behaviours, as well as potential threats to honey bee health and insight into the decline in the number of honey bees.
In addition, the Visual Analytics of Honey bee Behaviour (VizzzBees) has been launched to develop a framework for advanced analytics and visualisation of the swarm sensing data. The VizzzBees project aims to bring big data visual analytics to a large range of end users, so not only visual analytics experts can process the data and get insights from the data, but also novice users such as beekeepers and industrial partners can contribute their effort for advanced visual analytics. This project provides a full visual analytics pipeline including data management, analytics, visualisation, querying, and user interface design.
Since the volume of high-dimensional and spatio-temporal data collected from measurement devices as well as from simulated models is increasing, there is a need for building information models and analysing data to gain knowledge, make well informed decisions, and take responsive actions. Moreover, future data analytics and decision making infrastructure will be distributed and collaborative, allowing users to communicate, interact with each other, and coordinate their activities more effectively.
Technological advances and shifting interests of researchers provide Computer Supported Collaborative Work (CSCW) various potential and flexible tools to be integrated into shared environment heterogeneous systems, data structures, and information models, whose form and content may not be the same across all domains. The success of a CSCW system is decided by many features, including, but not limited to, heterogeneity, awareness, interaction, user interfaces, session management, system maintenance, and mutual understanding. Therefore, appropriate standard exchange mechanisms are needed to facilitate the full potential of sharing information models.
Besides, with the advances in technology of many display devices for augmented or virtual reality, multimodal immersive interfaces have the potential to be widely used to support collaborative visual analytics work. Although many collaborative platforms for immersive analytics have been developed over the years, little exploration has been undertaken into framework structures for systems that take into account the full pipeline of visual analytics processes in the context of ubiquitous data access in collaborative mixed-reality environments on a global scale.
The goal of the CoDIVA project is to provide a framework that facilitates collaborative visual analytics in immersive, interactive visualisation environments, supporting diverse users of various expertise to collect, visualise, analyse and share data and insights. Furthermore, multimodal interfaces will be integrated into our system, allowing users to interact with information models in more enhanced immersion level and effective collaboration. Dynamic collaborative visualisation management and gathering information environment are the core of the visual analytics system, which needs to provide information models and interactive techniques for collaborating in a heterogeneous framework of various interfaces and multiple users. Each interface can be a standalone application as well as a part in a collaborative scheme. Therefore, each interface can use its own local database that connects automatically to the central database server to synchronise all the data whenever there is a network connection. Besides honey bee data, we can consider the application of this framework in collaborative visual analytics on a large scale of big data such as geo-visualisation, climate and weather visualisation, bioinformatics, etc.